PPT-From Textual Entailment to Knowledgeable Machines

Peter Clark Allen Institute for Artificial Intelligence AI2 Mission achieve scientific breakthroughs by constructing AI systems with reasoning learning and reading capabilities 2 Overall Goals. Stephen C. Carlson. Australian Catholic University. June 30, 2015. The New Testament. The New Testament has been preserved in more manuscripts than any other work composed in Western antiquity. .. There are three main sources of data:. University . of. . Rome. “Tor Vergata”. Roma, Italy. Textual Entailment Recognition for Web Based Question-Answering. Operational. . Scenarios. What’s. the . weather. in Macao?. When. . is. QA4MRE, . and Machine Reading. Peter Clark. Vulcan Inc.. What is . Machine Reading?. Not (just). parsing + word senses. Construction of a . coherent representation. of the scene the text describes. By:Loay. . A.Hammad. I am knowledgeable because I know a lot.. When people wants help from me I help them by. advising . them and advisement needs. knowledge. . . I am knowledgeable . If you want to be knowledgeable you should read a lot of books . Private PSEP Evaluation. University of Nebraska—Lincoln Extension. Are you doing Initial Certification or Recertification?. Initial. Recertification. Have you attended pesticide safety education training sessions(s) in the past?. Asher Stern & . Ido. Dagan. ISCOL. June 2011, Israel. 1. Recognizing Textual Entailment (RTE). Given a text, . T. , and a hypothesis, . H. Does . T. entail . H. 2. T. : . An explosion caused by gas took place at a . (Excitement Project). Bernardo Magnini. (on behalf of the Excitement consortium). 1. STS workshop, NYC March 12-13 2012. Excitement Project. EXploring. Customer Interactions through Textual . EntailMENT. Peter Clark. Allen Institute for Artificial Intelligence (AI2). Mission: . achieve scientific breakthroughs by constructing AI systems with reasoning, learning, and reading capabilities. . 2. Overall Goals. Entailment and Paraphrasing. MSR Paraphrase Corpus. 1. Amrozi. accused his brother, whom he called "the witness", of deliberately distorting his evidence.. Referring to him as only "the witness", . Amrozi. ENTAILMENT. 4.1 Presupposition. 4.2 Types of Presupposition. 4.3 The Projection Problem. 4.4 Ordered Entailment. Introduction. : something that the speaker assumes to be the case before making an utterance. Why do we use machines?. Machines make doing work easier.. But they do not decrease the work that you do.. Instead, they . change the way you do work.. In general you trade more force for less distance or less force for more distance. including Finite State Machines.. Finite State MACHINES. Also known as Finite State Automata. Also known as FSM, or State . Machines. Facts about FSM, in general terms. Finite State Machines are important . Omer Levy . Ido. Dagan Jacob Goldberger. Bar-. Ilan. University, . Israel. Open IE. Extracts propositions from text. “…which makes aspirin relieve headaches.”. No supervision. No pre-defined schema. When we read, we are often asked to answer questions or express our ideas about the text.. Why use Explicit Textual Evidence. In order to let people know that we aren’t just making stuff up, we should always use Explicit Textual Evidence to support our answers, ideas, or opinions about texts we read..